A Symbolic Translation Pathway Model: Cultural Visual Analysis and Data Modelling of Miao Festival Costumes

The present study proposes a data-mining framework driven by symbolic networks to predict emotional resonance in images of ethnic costumes. In the context of the increasing integration of soft computing methodologies within the domain of cultural data science, the balancing act of maintaining computational efficiency whilst upholding cultural contextual integrity has become a critical challenge. The “Principles–Elements–Features–Meanings–Emotions” (PEFME′) symbolic translation pathway model is introduced through the case study of the Miao Traditional Festival Costumes (MTFCs) from Qiandongnan Prefecture, China. The model under discussion is grounded in Peirce’s triadic semiotics and Morris’s behavioural semiotics. It conceptualises five quantifiable layers of symbolic representation, namely visual design principles, structural elements, cultural features, interpreted meanings, and emotional responses. The development of a pipeline integrating NVivo, Excel, and Gephi has enabled the operationalisation of the model, thereby facilitating high-dimensional visual analytics. The empirical dataset under consideration comprises 59 high-resolution images of MTFCs, systematically coded through a multi-level framework that encompasses four visual principles, five element categories, and 17 sub-design features. The findings indicate a robust positive correlation between symbolic structural depth and users’ recognition of their cultural identity. The present research contributes a robust analytical methodology for verifying symbolic integrity, enhancing emotional interactivity, and supporting user-centred design in the digitisation of cultural heritage.